In today's business environment, huge amount of data may be generated. Such data may be stored in a database and managed by database system. The data may be stored in, for example, tables. The data may relate to, for example, employees, customers and customer orders. The data may be processed, as desired, to provide the desired information related to the data in the database. Such processing, for example, may provide analytics, such as statistical information as well as generating reports.
Data in the tables may be constantly changing or generated. For example, in transaction processing system, numerous transactions may occur in a day. The changes or new data (delta data) in the tables need to be loaded into the database. However, conventional databases do not provide for a delta data load function. This is because different businesses may have different types of tables or files, making it difficult to provide a delta data load solution by database providers for all the different users.
To update the database, full data loads are typically performed. For example, all data from the tables are loaded into the database. Due to the large amount of data, full data loads may require a tremendous amount of time. As such, full data loads are infrequently performed. Consequently, processing of data in the database may not reflect the latest data, resulting in inaccurate information.
Thus, a need exists for systems, methods, and apparatuses to effectively perform delta data loads in databases.